Congestion Reduction in EV Charger Placement Using Traffic Equilibrium Models
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914200086904832 |
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| author | Kara, Semih Sonmez, Yasin Kizilkale, Can Kurzhanskiy, Alex Martins, Nuno C. Arcak, Murat |
| author_facet | Kara, Semih Sonmez, Yasin Kizilkale, Can Kurzhanskiy, Alex Martins, Nuno C. Arcak, Murat |
| contents | Growing EV adoption can worsen traffic conditions if chargers are sited without regard to their impact on congestion. We study how to strategically place EV chargers to reduce congestion using two equilibrium models: one based on congestion games and one based on an atomic queueing simulation. We apply both models within a scalable greedy station-placement algorithm. Experiments show that this greedy scheme yields optimal or near-optimal congestion outcomes in realistic networks, even though global optimality is not guaranteed as we show with a counterexample. We also show that the queueing-based approach yields more realistic results than the congestion-game model, and we present a unified methodology that calibrates congestion delays from queue simulation and solves equilibrium in link-space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Congestion Reduction in EV Charger Placement Using Traffic Equilibrium Models Kara, Semih Sonmez, Yasin Kizilkale, Can Kurzhanskiy, Alex Martins, Nuno C. Arcak, Murat Systems and Control Artificial Intelligence Social and Information Networks Optimization and Control Growing EV adoption can worsen traffic conditions if chargers are sited without regard to their impact on congestion. We study how to strategically place EV chargers to reduce congestion using two equilibrium models: one based on congestion games and one based on an atomic queueing simulation. We apply both models within a scalable greedy station-placement algorithm. Experiments show that this greedy scheme yields optimal or near-optimal congestion outcomes in realistic networks, even though global optimality is not guaranteed as we show with a counterexample. We also show that the queueing-based approach yields more realistic results than the congestion-game model, and we present a unified methodology that calibrates congestion delays from queue simulation and solves equilibrium in link-space. |
| title | Congestion Reduction in EV Charger Placement Using Traffic Equilibrium Models |
| topic | Systems and Control Artificial Intelligence Social and Information Networks Optimization and Control |
| url | https://arxiv.org/abs/2512.12081 |